Endoscopic inspection system and method thereof

ABSTRACT

An endoscopic inspection system comprises: a switchable light source device for alternately providing first illumination light and second illumination light to illuminate an inspection location; an endoscope device for acquiring first image data of the inspection location under the illumination of the first illumination light, and acquiring second image data of the inspection location under the illumination of the second illumination light; a processor communicatively connected to the switchable light source device and the endoscope device, wherein the processor determines, according to the first image data and/or the second image data, whether the first image data and/or the second image data contains an abnormal region, and further generates determination data associated with the first image data and/or the second image data; and a display device communicatively connected to the processor for displaying the first image data and the second image data respectively according to a first display instruction and a second display instruction of the processor.

TECHNICAL HELD

The present invention relates to an endoscopic inspection system forabnormality inspection and a method thereof, and particularly, relatesto an endoscopic inspection system for abnormality inspectionalternatively switching between different illumination light and amethod thereof.

BACKGROUND

The conventional endoscopic inspection system can provide white light ornarrow band imaging (NBI) light according to operations of a user. Whenthe user chooses to perform operations under white light, the endoscopicinspection system will only perform detection to obtain white lightimage data. In this situation, when the user uses a white light mode, ifno abnormal region is found in the currently obtained white light image,the user will not realize that it is necessary to switch to an NBI lightmode to further determine the category of the abnormal region. Thus, ahigh-risk abnormal region could be possibly overlooked by the user. Inview of this, there is a need for an endoscopic inspection system forabnormality inspection and a method thereof, which can provide imagedata obtained under one kind of illumination light for a user to viewwhile performing detection to obtain image data under other kinds ofillumination light at the same time, and further determine whether thereis an abnormal region in the image data according to the obtained imagedata.

SUMMARY

In order to solve the above problem, one scheme of the present inventionis to provide an endoscopic inspection system for abnormality inspectionand a method thereof, which can provide image data obtained under onekind of illumination light for a user to view while performing detectionto obtain image data under other kinds of illumination light at the sametime, and further determine whether there is an abnormal region in theimage data according to the obtained image data.

On the basis of the disclosed scheme, the present invention provides anendoscopic inspection system, comprising: a switchable light sourcedevice for alternately providing first illumination light and secondillumination light to illuminate an inspection location; an endoscopedevice for acquiring first image data of the inspection location underthe illumination of the first illumination light, and acquiring secondimage data of the inspection location under the illumination of thesecond illumination light; a processor communicatively connected to theswitchable light source device and the endoscope device, wherein theprocessor determines, according to the first image data and/or thesecond image data, whether the first image data and/or the second imagedata contains an abnormal region, and further generates determinationdata associated with the first image data and/or the second image data;and a display device communicatively connected to the processor fordisplaying the first image data and the second image data respectivelyaccording to a first display instruction and a second displayinstruction of the processor.

In one preferred embodiment of the present invention, the firstillumination light is white light, and the second illumination light isnarrow band imaging (NM) blue light or NBI green light.

In one preferred embodiment of the present invention, the switchablelight source device alternately provides the first illumination lightand the second illumination light at a switching frequency greater than30 Hz.

In one preferred embodiment of the present invention, if the processordetermines that the first image data and/or the second image datacontains an abnormal region, the display device is instructed to markthe abnormal region on the first image data and/or the second image dataaccording to the determination data.

In one preferred embodiment of the present invention, the determinationdata comprises probability data, and when marking the abnormal region onthe first image data and/or the second image data, the display devicedisplays the probability data on the first image data and/or the secondimage data at the same time.

In one preferred embodiment of the present invention, the determinationdata comprises a type label associated with the first image data, thesecond image data, or the abnormal region.

In one preferred embodiment of the present invention, if the processordetermines that the first image data and/or the second image datacontains an abnormal region, the processor issues an alert signal.

In one preferred embodiment of the present invention, the processorcomprises a convolutional neural network module, wherein the processordetermines whether the first image data and/or the second image datacontains an abnormal region by means of the convolutional neural networkmodule, and further generates the determination data.

In one preferred embodiment of the present invention, the processorcomprises a training module, and the training module trains theconvolutional neural network module by means of a plurality pieces oftraining data; wherein each of the plurality pieces of training data isassociated with a type label.

In one preferred embodiment of the present invention, the processorcomprises a training module, and the training module acquires targetregion data from the first image data and/or the second image dataaccording to a control instruction; wherein the training moduleassociates the target region data with a type label according to a firstdetermination instruction; wherein the training module trains theconvolutional neural network module by means of the target region dataassociated with the type label.

In one preferred embodiment of the present invention, the processorcomprises a training module, and the training module associates thefirst image data and/or the second image data with a type labelaccording to a second determination instruction; wherein the trainingmodule trains the convolutional neural network module by means of thefirst image data and/or the second image data associated with the typelabel.

According to the objective of the present invention, an endoscopicinspection method is further provided, the method comprising:alternately providing, by a switchable light source device, firstillumination light and second illumination light to illuminate aninspection location; acquiring, by an endoscope device, first image dataof the inspection location under the illumination of the firstillumination light, and acquiring second image data of the inspectionlocation under the illumination of the second illumination light;determining, according to the first image data and/or the second imagedata by a processor communicatively connected to the switchable lightsource device and the endoscope device, whether the first image dataand/or the second image data contains an abnormal region, and furthergenerating determination data; associating, by the processor, thedetermination data with the first image data and/or the second imagedata; and displaying, by a display device communicatively connected tothe processor, the first image data and the second image datarespectively according to a first display instruction and a seconddisplay instruction of the processor.

In one preferred embodiment of the present invention, the firstillumination light is white light, and the second illumination light isNBI blue light or NBI green light.

In one preferred embodiment of the present invention, the switchablelight source device alternately provides the first illumination lightand the second illumination light at a switching frequency greater than30 Hz.

In one preferred embodiment of the present invention, the endoscopicinspection method for abnormality inspection further comprises: if theprocessor determines that the first image data and/or the second imagedata contains an abnormal region, instructing, by the processor, thedisplay device to mark the abnormal region on the first image dataand/or the second image data according to the determination data.

In one preferred embodiment of the present invention, the determinationdata comprises probability data, and when marking the abnormal region onthe first image data and/or the second image data, the display devicedisplays the probability data on the first image data and/or the secondimage data at the same time.

In one preferred embodiment of the present invention, the determinationdata comprises a type label associated with the first image data, thesecond image data, or the abnormal region.

In one preferred embodiment of the present invention, the endoscopicinspection method for abnormality inspection further comprises: if theprocessor determines that the first image data and/or the second imagedata contains an abnormal region, the processor issues an alert signal.

In one preferred embodiment of the present invention, the processorcomprises a convolutional neural network module, wherein the processordetermines whether the first image data and/or the second image datacontains an abnormal region by means of the convolutional neural networkmodule, and further generates the determination data.

In one preferred embodiment of the present invention, the endoscopicinspection method for abnormality inspection further comprises:training, by a training module of the processor, the convolutionalneural network module by means of a plurality pieces of training data;wherein each of the plurality pieces of training data is associated witha type label.

In one preferred embodiment of the present invention, the endoscopicinspection method for abnormality inspection further comprises:acquiring, by a training module of the processor, target region datafrom the first image data and/or the second image data according to acontrol instruction; associating, by the training module, the targetregion data with a type label according to a first determinationinstruction; and training, by the training module, the convolutionalneural network module by means of the target region data associated withthe type label.

In one preferred embodiment of the present invention, the endoscopicinspection method for abnormality inspection further comprises:associating, by a training module of the processor, the first image dataand/or the second image data with a type label according to a seconddetermination instruction; and training, by the training module, theconvolutional neural network module by means of the first image dataand/or the second image data associated with the type label.

The foregoing aspects and other aspects of the present invention willbecome apparent in accordance with the detailed description of thefollowing non-limitative particular embodiments and with reference tothe accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a system architecture diagram of a particular embodiment of anendoscopic inspection system of the present invention.

FIG. 2 is an architecture diagram of a particular embodiment of aprocessor of the endoscopic inspection system of the present invention.

FIG. 3 is a schematic diagram of a particular embodiment of theendoscopic inspection system of the present invention.

FIG. 4 is a schematic diagram of a particular embodiment of training theprocessor.

FIG. 5 is a schematic diagram of a particular embodiment of using theprocessor to perform determination.

FIG. 6 is a flowchart of a particular embodiment of an endoscopicinspection method of the present invention.

DESCRIPTION OF EMBODIMENTS

Referring to FIG. 1 , a system architecture diagram according to aparticular embodiment of an endoscopic inspection system of the presentinvention is exemplarily illustrated. As shown in the embodimentillustrated in FIG. 1 , the endoscopic inspection system 100 comprises adatabase 110, a processor 120, a switchable light source device 130, anendoscope device 140, and a display device 150. The processor 120 iscommunicatively connected to the database 110, the switchable lightsource device 130, the endoscope device 140, and the display device 150.The switchable light source device 130 alternately provides firstillumination light and second illumination light to illuminate aninspection location. The endoscope device 140 can acquire (or performdetection to obtain) first image data of the inspection location underthe illumination of the first illumination light, and acquire (orperform detection to obtain) second image data of the inspectionlocation under the illumination of the second illumination light. Theprocessor 120 can determine, according to the first image data and/orthe second image data, whether the first image data and/or the secondimage data contains an abnormal region, and the processor 120 furthergenerates determination data associated with the first image data and/orthe second image data according to a determination result. Ifdetermining that the first image data and/or the second image datacontains an abnormal region, the processor 120 can also issue an alertsignal. In addition, the processor 120 can also store the first imagedata, the second image data, and the determination data acquired (orobtained) by the endoscope device 140 to the database 110. The displaydevice 150 can display the first image data according to a first displayinstruction, and can display the second image data according to a seconddisplay instruction. Hence, a user can choose to view the first imagedata or the second image data according to requirements. In otherparticular embodiments, the alert signal may be a sound, light, animage, or the like, but is not limited thereto. It should be understoodthat the abnormal region comprised in the first image data and/or thesecond image data corresponds to (or represents) an abnormal region onthe inspection location. In a particular embodiment, the inspectionlocation is the digestive tract (e.g., the large intestine), and theabnormal region is a polyp.

It should be understood that because the switchable light source device130 continuously switches between the first illumination light and thesecond illumination, when the user is viewing the first image data, theendoscopic inspection device 140 can still acquire (or perform detectionto obtain) the second image data when the switchable light source device130 switches to the second illumination light. Similarly, when the useris viewing the second image data, the endoscopic inspection device 140can still acquire (or perform detection to obtain) the first image datawhen the switchable light source device 130 switches to the firstillumination light. Hence, no matter whether the user chooses to viewthe first image data or the second image data, the endoscopic inspectiondevice 140 can continuously acquire (or perform detection to obtain) thefirst image data and the second image data for the processor 120 toperform determination.

It should be understood that when the user is viewing the first imagedata or the second image data, the processor 120 performs abnormalregion determination on the first image data and/or the second imagedata in a substantially synchronized manner. Hence, the endoscopicinspection system 100 enables the user to be aware of whether thecurrently obtained first image data and/or the second image datacontains an abnormal region by means of the alert signal and/or thedetermination data, while the user obtains the first image data or thesecond image data by means of detection performed by the endoscopedevice 140. In this way, the user can perform an appropriate treatmenton the abnormal region immediately (e.g., immediate removal or notreatment at that time). In a particular embodiment, the determinationdata comprises a type label. The type label is associated with the firstimage data and/or the second image data, and indicates that the firstimage data and/or the second image data is an image of a normal region(that is, no abnormal region is comprised). In a particular embodiment,the type label is associated with the first image data and/or the secondimage data, and indicates that the first image data and/or the secondimage data is an image comprising the abnormal region. In a particularembodiment, the type label is associated with a target region in thefirst image data and/or the second image data, and indicates thecategory of the target region (e.g., the target region is a normalregion or an abnormal region). If the abnormal region comprisesdifferent kinds of abnormal regions, the type label indicates which kindof abnormal region the target region is.

In a particular embodiment, the first illumination light is white light,and the second illumination light is narrow band imaging (NBI) light. Inother particular embodiments, the NBI light is NBI blue light or NMgreen light, but is not limited thereto. In a particular embodiment, theswitchable light source device 130 alternately provides the firstillumination light and the second illumination light at a switchingfrequency. In a particular embodiment, since the switching frequency ishigh enough, when the user chooses to view the first image data, thedisplayed first image data is visually continuous images. When the userchooses to view the second image data, the displayed second image datais also visually continuous images. In a particular embodiment, theswitching frequency is greater than 30 Hz because when the switchingfrequency is less than 30 Hz, the image capture frequency of the firstimage data and the image capture frequency of the second image data willbe less than 15 Hz, which will make the images not visually continuous.In a particular embodiment, the switching frequency is less than 240 Hz.In a particular embodiment, the switching frequency is 60 Hz. Hence, theimage capture frequency of the first image data and the image capturefrequency of the second image data will be 30 Hz. It should beunderstood that the switching frequency is not limited to 60 Hz, and canbe configured to be other values as required. In a particularembodiment, the switching frequency may be configured by the userhimself.

In a particular embodiment, the switchable light source device 130 hasfirst illumination light source and second illumination light source.The switchable light source device 130 respectively provides the firstillumination light and the second illumination light by switching thefirst illumination light source and the second illumination lightsource. In a particular embodiment, the switchable light source device130 only has a single light source (e.g., a white light source, but notlimited thereto) and a light filtering device. The switchable lightsource device 130 switches the first illumination light and the secondillumination light by manipulating the light filtering device. In aparticular embodiment, illumination light provided by the single lightsource is the first illumination light, and illumination light generatedafter the filtering of the light filtering device is the secondillumination light. In another particular embodiment, the lightfiltering device has two light filtering portions; illumination lightgenerated after the filtering of the first filtering portion is thefirst illumination light, and illumination light generated after thefiltering of the second filtering portion is the second illuminationlight.

In a particular embodiment, if the processor 120 determines that thefirst image data and/or the second image data contains the abnormalregion, the display device 150 is instructed to mark the abnormal regionon the first image data and/or the second image data according to thedetermination data. The marking method may comprise, but is not limitedto, for example, indicating the position of the abnormal region, ormarking the contour or the boundary of the abnormal region on the firstimage data and/or the second image data.

In a particular embodiment, the determination data comprises probabilitydata. The probability data is acquired by the determination of theprocessor 120 according to the first image data and/or the second imagedata. In a particular embodiment, the probability data represents theprobability that the abnormal region determined by the processor 120 isindeed an abnormal region. In a particular embodiment, the probabilitydata represents the probability that the first image data and/or thesecond image data indeed do not contain an abnormal region. When markingthe abnormal region on the first image data and/or the second imagedata, the display device 150 can display the probability data on thefirst image data and/or the second image data at the same time.

Referring to FIG. 2 , an architecture diagram of a particular embodimentof the processor of the endoscopic inspection system of the presentinvention is exemplarily illustrated. As shown in the embodimentillustrated in FIG. 2 , the processor 220 is communicatively connectedto the database 210. The database 210 stores a plurality pieces oftraining data, and each of the plurality pieces of training data isassociated with a type label. The type label of respective training datarecords the type of the content of the training data.

In a particular embodiment, the endoscopic inspection system is used todetermine whether there is a polyp (the polyp represents the abnormalregion) in the digestive system. The used training data comprise imagedata of normal regions in the digestive system and image data of polypsin the digestive system. The processor 220 can determine whether thetraining data is the image data of the normal region or the image dataof the polyp according to the type label associated with respectivetraining data. In another particular embodiment, the endoscopicinspection system is used to determine whether there is a polyp in thedigestive system (no matter what the category of the polyp is, the polyprepresents a kind of abnormal region), and the category of the polyp(e.g. a hyperplastic polyp or an adenomatous polyp). The used trainingdata comprise image data of normal regions in the digestive system,image data of a first category of polyps (e.g., hyperplastic polyps) inthe digestive system, and image data of a second category of polyp (e.g.adenomatous polyp) in the digestive system. The processor 220 candetermine whether the training data is the image data of the normalregion, the image data of the first category of polyp, or the image dataof the second category of polyp according to the type label associatedwith respective training data.

In the embodiment shown in FIG. 2 , the processor 220 comprises atraining module 222 and a convolutional neural network module 224. Theprocessor 220 can train the convolutional neural network module 224 bymeans of the training module 222. Hence, the processor 220 can determinewhether the first image data and/or the second image data comprise(s) anabnormal region by means of the convolutional neural network module 224,and further generate the determination data. The training module 222trains the convolutional neural network module 224 by means of theplurality pieces of training data stored in the database 210, and eachof the plurality pieces of training data is associated with a typelabel. In a particular embodiment, the processor 220 implements thetraining module 222 and the convolutional neural network module 224 in amanner of coordinated operation of hardware and software.

In a particular embodiment, the user can perform determination withrespect to the first image data and/or the second image data by himself,and can store the first image data and/or the second image data into thedatabase 210 as one of the training data after the determination. Adetermination result made by the user for the first image data and/orthe second image data is the type label of the first image data and/orthe second image data.

In a particular embodiment, the user can mark the boundary or thecontour of a target region (e.g., an abnormal region) on the first imagedata and/or the second image data by providing a control instruction.Hence, the training module 222 can acquire target region data from thefirst image data and/or the second image data according to the controlinstruction. The training module 222 can then associate the targetregion data with a type label according to a first determinationinstruction provided by the user. The type label is the result ofdetermination made by the user for the target region, which indicateswhether the target region is a normal region or an abnormal region (ifthe abnormal region comprises different kinds of abnormal regions, thetype label can indicate which kind of abnormal region the target regionis). The training module 222 can also train the convolutional neuralnetwork module 224 by means of the target region data associated withthe type label.

In a particular embodiment, if the user determines that the first imagedata and/or the second image data does not comprise the abnormal regionand is an image of a normal region, the training module 222 canassociate the first image data and/or the second image data with a typelabel according to a second determination instruction provided by theuser. The type label is the determination result of the user for thefirst image data and/or the second image data. It should be understoodthat if the user determines that the first image data and/or the secondimage data contains the abnormal region, the user can also choose todirectly associate the first image data and/or the second image datawith a type label without marking the boundary of the abnormal region onthe first image data and/or the second image data in advance.

Referring to FIG. 3 , a schematic diagram of a particular embodiment ofthe endoscopic inspection system of the present invention is shown. Asshown in the embodiment illustrated in FIG. 3 , since the switchablelight source device alternately provides the first illumination lightand the second illumination light, the endoscope device can sequentiallyperform detection to obtain the first image data 352, the second imagedata 354, the first image data 356, and the second image data 358. Afterthe endoscope device performs detection to obtain the first image data352 and 356 respectively, the first image data 352 and 356 are thenhanded over to the processor for determination. In addition, if the userchooses to view the first image data, while the processor performsdetermination with respect to the first image data 352 and 356, thefirst image data 352 and 356 are also provided for viewing by the userby means of the display device at the same time. Because the switchingfrequency of the switchable light source device is high enough, thefirst image data 352 and 356 are visually continuous images. Similarly,after the endoscope device performs detection to obtain the second imagedata 354 and 358 respectively, the second image data 354 and 358 arethen handed over to the processor for determination. In addition, if theuser chooses to view the second image data, while the processor performsdetermination with respect to the second image data 354 and 358, thesecond image data 354 and 358 are also provided for viewing by the userby means of the display device at the same time. Because the switchingfrequency of the switchable light source device is high enough, thesecond image data 354 and 358 are visually continuous images.

Referring to FIG. 4 , a schematic diagram of a particular embodiment oftraining the processor is shown. As shown in the embodiment illustratedin FIG. 4 , the first illumination light is white light, and the secondillumination light is NBI light. The first image data is white lightimage data, and the second image data is NBI image data. When whitelight image data 452 is obtained, the user can mark the white lightimage data 452 to indicate whether there is a polyp, and then theprocessor is trained by means of the white light image data 452comprising the mark. In a particular embodiment, when marking a polyp,the user can further mark the position of the polyp and/or the boundarythereof. In a particular embodiment, when marking a polyp, the user canfurther mark the category of the polyp. In a particular embodiment, theprocessor comprises an artificial intelligence module (e.g., aconvolutional neural network module). In addition, As shown in theembodiment illustrated in FIG. 4 , when NBI light image data 454 isobtained, the user can mark the NBI light image data 454 to indicatewhether there is a polyp, and indicate the category of the polyp (e.g.,a hyperplastic polyp or an adenomatous polyp), and then the processor istrained by means of the NBI light image data 454 comprising the marks.In a particular embodiment, when marking a polyp, the user can furthermark the position of the polyp and/or the boundary thereof. It should beunderstood that no matter what category the polyp is, it represents akind of abnormal region. An abnormal region comprised in image data is aregion corresponding to the polyp in the image data.

Referring to FIG. 5 , a schematic diagram of a particular embodiment ofusing the processor to perform determination (or deduction) is shown. Asshown in the embodiment illustrated in FIG. 5 , the first illuminationlight is white light, and the second illumination light is NBI light.The first image data is white light image data, and the second imagedata is NBI image data. When white light image data 552 is obtained, theprocessor can determine (or deduce) whether there is a polyp thereonaccording to the white light image data 552. When NBI light image data554 is obtained, the processor can determine (or deduce) whether thereis a polyp thereon according to the NBI light image data 554, andfurther determine (or deduce) the category of the polyp. In a particularembodiment, the processor comprises an artificial intelligence module(e.g., a convolutional neural network module), and the processorperforms image data determination (or known as deduction) by means ofthe artificial intelligence module. In a particular embodiment, theprocessor can further determine (or deduce) the category of a polypaccording to the white light image data 552. It should be understoodthat no matter what category the polyp is, it represents a kind ofabnormal region.

Referring to FIG. 6 , a flowchart of a particular embodiment of anendoscopic inspection method of the present invention is exemplarilyillustrated. As shown in the embodiment illustrated in FIG. 6 , theendoscopic inspection method 600 is applied to an endoscopic inspectionsystem, and the endoscopic inspection system comprises a switchablelight source device, an endoscope device, a processor, and a displaydevice. The endoscopic inspection method 600 starts from step 610; theswitchable light source device alternately provides first illuminationlight and second illumination light to illuminate an inspectionlocation. In a particular embodiment, the first illumination light iswhite light, and the second illumination light is NBI light. In otherparticular embodiments, the NBI light is NBI blue light or NBI greenlight, but is not limited thereto.

After performing step 610, step 620 is then performed; the endoscopedevice acquires (or performs detection to obtain) first image data underthe illumination of the first illumination light, and acquires (orperforms detection to obtain) second image data under the illuminationof the second illumination light. Then, step 630 is performed; theprocessor communicatively connected to the switchable light sourcedevice and the endoscope device determines, according to the first imagedata and/or the second image data, whether the first image data and/orthe second image data contains an abnormal region, and further generatesdetermination data. In a particular embodiment, the determination datacomprises a type label, wherein the type label is associated with thefirst image data, the second image data, or the abnormal region.

After performing step 630, step 640 is then performed; the processorassociates the determination data with the first image data and/or thesecond image data. Then, step 650 is performed; the display devicecommunicatively connected to the processor respectively displays thefirst image data and the second image data according to a first displayinstruction and a second display instruction of the processor. In aparticular embodiment, the switchable light source device alternatelyprovides the first illumination light and the second illumination lightat a switching frequency. Since the switching frequency is high enough,when the user chooses to view the first image data, the displayed firstimage data is visually continuous images. When the user chooses to viewthe second image data, the displayed second image data is also visuallycontinuous images. In a particular embodiment, the switching frequencyis 60 Hz.

After performing step 650, step 660 is then performed; if the processordetermines that the first image data and/or the second image datacontains an abnormal region, the processor instructs the display deviceto mark the abnormal region on the first image data and/or the secondimage data according to the determination data. In a particularembodiment, the determination data comprises probability data, and whenmarking the abnormal region on the first image data and/or the secondimage data, the display device can display the probability data on thefirst image data and/or the second image data at the same time. In aparticular embodiment, the probability data represents the probabilitythat the abnormal region determined by the processor is indeed anabnormal region. In a particular embodiment, the probability datarepresents the probability that the first image data and/or the secondimage data indeed do not contain an abnormal region. Finally, step 670is performed; if the processor determines that the first image dataand/or the second image data contains an abnormal region, the processorissues an alert signal.

It should be understood that in other particular embodiments, the orderof step 630 to step 670 can be adjusted according to requirements. In aparticular embodiment, the processor of the endoscopic inspection systemcomprises a convolutional neural network module and a training module.The processor determines whether the first image data and/or the secondimage data contains an abnormal region by means of the convolutionalneural network module, and further generates the determination data. Ina particular embodiment, the endoscopic inspection method 600 furthercomprises: the training module of the processor training theconvolutional neural network module by means of a plurality pieces oftraining data. Each of the plurality pieces of training data isassociated with a type label.

In a particular embodiment, the endoscopic inspection method 600 furthercomprises: the training module acquiring target region data from thefirst image data and/or the second image data according to a controlinstruction; the training module associating the target region data witha type label according to a first determination instruction; and thetraining module training the convolutional neural network module bymeans of the target region data associated with the type label. In aparticular embodiment, the endoscopic inspection method 600 furthercomprises: the training module associating the first image data and/orthe second image data with a type label according to a seconddetermination instruction; and the training module training theconvolutional neural network module by means of the first image dataand/or the second image data associated with the type label.

Hence, the endoscopic inspection system and the method thereof of thepresent invention are described in the above description and drawings.It should be understood that each particular embodiment of the presentinvention is for illustrative purpose only, and various changes can bemade without departing from the scope and spirit of the claims of thepresent invention, and should fall within the scope of the presentinvention. Therefore, each particular embodiment described in thedescription is not intended to limit the present invention, and thescope and spirit of the present invention are disclosed in the followingclaims.

LIST OF REFERENCE NUMERALS

-   -   100 Endoscopic inspection system    -   110 Database    -   120 Processor    -   130 Switchable light source device    -   140 Endoscope device    -   150 Display device    -   210 Database    -   220 Processor    -   222 Training module    -   224 Convolutional neural network module    -   352 First image data    -   354 Second image data    -   356 First image data    -   358 Second image data    -   452 White light image data    -   454 NBI light image data    -   552 White light image data    -   554 NBI light image data    -   600 Endoscopic inspection method    -   610 Step    -   620 Step    -   630 Step    -   640 Step    -   650 Step    -   660 Step    -   670 Step    -   Deposit of Biological Material    -   None

What is claimed is:
 1. An endoscopic inspection system, comprising: aswitchable light source device for alternately providing firstillumination light and second illumination light to illuminate aninspection location; an endoscope device for acquiring first image dataof the inspection location under the illumination of the firstillumination light and acquiring second image data of the inspectionlocation under the illumination of the second illumination light; aprocessor communicatively connected to the switchable light sourcedevice and the endoscope device, wherein the processor determines,according to the first image data and/or the second image data, whetherthe first image data and/or the second image data contains an abnormalregion, and further generates determination data associated with thefirst image data and/or the second image data; and a display devicecommunicatively connected to the processor for displaying the firstimage data and the second image data respectively according to a firstdisplay instruction and a second display instruction of the processor;wherein the switchable light source device alternately provides thefirst illumination light and the second illumination light at aswitching frequency greater than 30 Hz.
 2. The endoscopic inspectionsystem of claim 1, wherein the first illumination light is white light,and the second illumination light is narrow band imaging (NBI) bluelight or NBI green light.
 3. The endoscopic inspection system of claim1, wherein if the processor determines that the first image data and/orthe second image data contains an abnormal region, the display device isinstructed to mark the abnormal region on the first image data and/orthe second image data according to the determination data.
 4. Theendoscopic inspection system of claim 3, wherein the determination datacomprises probability data, and when marking the abnormal region on thefirst image data and/or the second image data, the display devicedisplays the probability data on the first image data and/or the secondimage data at the same time.
 5. The endoscopic inspection system ofclaim 1, wherein the determination data comprises a type label, and thetype label is associated with the first image data, the second imagedata, or the abnormal region.
 6. The endoscopic inspection system ofclaim 1, wherein if the processor determines that the first image dataand/or the second image data contains an abnormal region, the processorissues an alert signal.
 7. The endoscopic inspection system of claim 1,wherein the processor comprises a convolutional neural network module,and the processor determines whether the first image data and/or thesecond image data contains an abnormal region by means of theconvolutional neural network module, and further generates thedetermination data.
 8. The endoscopic inspection system of claim 7,wherein the processor comprises a training module, and the trainingmodule trains the convolutional neural network module by means of aplurality pieces of training data; wherein each of the plurality piecesof training data is associated with a type label.
 9. The endoscopicinspection system of claim 7, wherein the processor comprises a trainingmodule, and the training module acquires target region data from thefirst image data and/or the second image data according to a controlinstruction; wherein the training module associates the target regiondata with a type label according to a first determination instruction;wherein the training module trains the convolutional neural networkmodule by means of the target region data associated with the typelabel.
 10. The endoscopic inspection system of claim 7, wherein theprocessor comprises a training module, and the training moduleassociates the first image data and/or the second image data with a typelabel according to a second determination instruction; wherein thetraining module trains the convolutional neural network module by meansof the first image data and/or the second image data associated with thetype label.
 11. An endoscopic inspection method, comprising: alternatelyproviding, by a switchable light source device, first illumination lightand second illumination light to illuminate an inspection location;acquiring, by an endoscope device, first image data of the inspectionlocation under the illumination of the first illumination light, andacquiring second image data of the inspection location under theillumination of the second illumination light; determining, according tothe first image data and/or the second image data by a processorcommunicatively connected to the switchable light source device and theendoscope device, whether the first image data and/or the second imagedata contains an abnormal region, and further generating determinationdata; associating, by the processor, the determination data with thefirst image data and/or the second image data; and displaying, by adisplay device communicatively connected to the processor, the firstimage data and the second image data respectively according to a firstdisplay instruction and a second display instruction of the processor;wherein the switchable light source device alternately provides thefirst illumination light and the second illumination light at aswitching frequency greater than 30 Hz.
 12. The endoscopic inspectionmethod of claim 11, wherein the first illumination light is white light,and the second illumination light is narrow band imaging blue light ornarrow band imaging green light.
 13. The endoscopic inspection method ofclaim 11, further comprising: if the processor determines that the firstimage data and/or the second image data contains an abnormal region,instructing, by the processor, the display device to mark the abnormalregion on the first image data and/or the second image data according tothe determination data.
 14. The endoscopic inspection method of claim13, wherein the determination data comprises probability data, and whenmarking the abnormal region on the first image data and/or the secondimage data, the display device displays the probability data on thefirst image data and/or the second image data at the same time.
 15. Theendoscopic inspection method of claim 11, wherein the determination datacomprises a type label, and the type label is associated with the firstimage data, the second image data, or the abnormal region.
 16. Theendoscopic inspection method of claim 11, further comprising: if theprocessor determines that the first image data and/or the second imagedata contains an abnormal region, issuing, by the processor, an alertsignal.
 17. The endoscopic inspection method of claim 11, wherein theprocessor comprises a convolutional neural network module, and theprocessor determines whether the first image data and/or the secondimage data contains an abnormal region by means of the convolutionalneural network module, and further generates the determination data. 18.The endoscopic inspection method of claim 17, further comprising:training, by a training module of the processor, the convolutionalneural network module by means of a plurality pieces of training data;wherein each of the plurality pieces of training data is associated witha type label.
 19. The endoscopic inspection method of claim 17, furthercomprising: acquiring, by a training module of the processor, targetregion data from the first image data and/or the second image dataaccording to a control instruction; associating, by the training module,the target region data with a type label according to a firstdetermination instruction; and training, by the training module, theconvolutional neural network module by means of the target region dataassociated with the type label.
 20. The endoscopic inspection method ofclaim 17, further comprising: associating, by a training module of theprocessor, the first image data and/or the second image data with a typelabel according to a second determination instruction; and training, bythe training module, the convolutional neural network module by means ofthe first image data and/or the second image data associated with thetype label.